adsabs/AstroCLIMB huggingface dataset:
Paper: An Interpretable Machine-learning Framework for Modeling High-resolution Spectroscopic Data 
Authors: Michael Gully-Santiago, Caroline V. Morley
Figure caption: Line-by-line dissection of the 2MASS J08173001−6155158 IGRINS spectrum. The top panel compares the native Sonora−Bobcat spectrum to the blasé clone. We match the RV shift of the observed spectrum (thick black line) and convolve the initial and learned Sonora template to the large ﹩v\sin i﹩. The bottom panel shows the locations and extents of line mismatches at the native resolution of Sonora.
Paper: An Interpretable Machine-learning Framework for Modeling High-resolution Spectroscopic Data 
Authors: Michael Gully-Santiago, Caroline V. Morley
Figure caption: End-to-end training on a portion of the HPF spectrum of WASP 69. The top two panels show the cloning from Step 1 of the visual guide. The middle two panels depict the before-and-after of the instrument-convolved and resampled joint model illustrated in Step 4 of the visual guide. The final two panels show the underlying semiempirical models learned in the process.
Paper: An Interpretable Machine-learning Framework for Modeling High-resolution Spectroscopic Data 
Authors: Michael Gully-Santiago, Caroline V. Morley
Figure caption: The native-resolution semiempirical model transfer-learned from WASP 69 HPF data. The revised model can be viewed as a super-resolution deconvolution of the HPF spectrum.
Paper: Effects of Active Galactic Nucleus Feedback on Cold Gas Depletion and Quenching of Central Galaxies
Authors: Wenlin Ma, Kexin Liu, Hong Guo, Weiguang Cui, Michael G. Jones, Jing Wang, Le Zhang, Romeel Davé
Figure caption: Left: relation between SFR and M HI, scaled by the corresponding values on the SFMS, i.e., SFR/SFRMS and M HI/M HI,MS. Right: similar relation between SFR and ﹩{M}_{{{\rm{H}}}_{2}}﹩. The measurements of TNG, SIMBA, G21, and xCOLD GASS are shown as blue circles, red squares, yellow lines, and peach lines, respectively. The peach dashed line in the right panel shows the upper limits of the xCOLD GASS nondetections. The data points shown are the median values and the errors are estimated from the 16th to 84th percentile ranges. For clarity, the SFR measurements for TNG are shifted leftwards by 0.1 dex. The horizontal dotted lines indicate the positions of the HIMS (left) and H2MS (right), while the vertical ones are for the SFMS. Only galaxies with M * > 109.5  M ☉ are included in both observations and simulations.
Paper: Effects of Active Galactic Nucleus Feedback on Cold Gas Depletion and Quenching of Central Galaxies
Authors: Wenlin Ma, Kexin Liu, Hong Guo, Weiguang Cui, Michael G. Jones, Jing Wang, Le Zhang, Romeel Davé
Figure caption: The relation between ﹩{\rm{\Delta }}\mathrm{log}\,\mathrm{SFR}﹩ and ﹩{\rm{\Delta }}\mathrm{log}\,{M}_{\mathrm{HI}}﹩ for TNG (upper panels) and SIMBA (bottom panels). The galaxy samples are separated into two M BH (left) and f edd (right) bins, shown as different colors. The filled circles and squares are for the total H I mass M HI as in Figure 4, while solid and dotted lines represent the corresponding H I mass measurements within 2R half and R half, denoted as M HI,2R half  and M HI,R half , respectively.
Paper: Effects of Active Galactic Nucleus Feedback on Cold Gas Depletion and Quenching of Central Galaxies
Authors: Wenlin Ma, Kexin Liu, Hong Guo, Weiguang Cui, Michael G. Jones, Jing Wang, Le Zhang, Romeel Davé
Figure caption: The 3D H I mass density profile ρ HI(r/R half) for TNG (left) and SIMBA (right), where the distance to galaxy center r is scaled by the corresponding stellar half-mass radius R half. SFGs and QGs with M * ≥ 109.5  M ☉ are represented by blue circles and red squares, respectively. We show the median values along with the 20th–80th percentile ranges. The 3D H2 mass density profiles in TNG are also included in the left panel, shown as blue solid (SFGs) and red dotted (QGs) lines. The vertical dashed line in each panel indicates the position of r = 2R half, which is used to compute the gas masses within the galaxy radii.
Paper: Effects of Active Galactic Nucleus Feedback on Cold Gas Depletion and Quenching of Central Galaxies
Authors: Wenlin Ma, Kexin Liu, Hong Guo, Weiguang Cui, Michael G. Jones, Jing Wang, Le Zhang, Romeel Davé
Figure caption: Similar to Figure 7, but for the H2 gas. We also show the observational measurements of xCOLD GASS for comparison.
Paper: Effects of Active Galactic Nucleus Feedback on Cold Gas Depletion and Quenching of Central Galaxies
Authors: Wenlin Ma, Kexin Liu, Hong Guo, Weiguang Cui, Michael G. Jones, Jing Wang, Le Zhang, Romeel Davé
Figure caption: Dependences of Δlog M HI,2R half  (top panels), ﹩{\rm{\Delta }}\mathrm{log}\,{M}_{{{\rm{H}}}_{2},2{R}_{\mathrm{half}}}﹩ (middle panels), and ﹩{\rm{\Delta }}\mathrm{log}\,\mathrm{SFR}﹩ (bottom panels) on M BH (left columns) and f edd (right columns), for TNG (blue circles) and SIMBA (red squares).
Paper: Effects of Active Galactic Nucleus Feedback on Cold Gas Depletion and Quenching of Central Galaxies
Authors: Wenlin Ma, Kexin Liu, Hong Guo, Weiguang Cui, Michael G. Jones, Jing Wang, Le Zhang, Romeel Davé
Figure caption: Distribution of galaxies in the planes of M HI–M * (top panels) and ﹩{M}_{{{\rm{H}}}_{2}}﹩–M * (bottom panels) for TNG (left) and SIMBA (right), respectively. The H I and H2 main sequences are defined as the corresponding median values for SFGs in different M * bins, shown as the black points. Distributions of SFGs and QGs are represented by the contour and gray dots in each panel. The probability distribution of SFGs is represented by the logarithmic color scales. The red dotted lines are HIMS and H2MS defined in G21 and Janowiecki et al. (2020), shown as Equations (16) and (17).
Paper: Effects of Active Galactic Nucleus Feedback on Cold Gas Depletion and Quenching of Central Galaxies
Authors: Wenlin Ma, Kexin Liu, Hong Guo, Weiguang Cui, Michael G. Jones, Jing Wang, Le Zhang, Romeel Davé
Figure caption: Distribution of galaxies as a function of M BH and f edd for TNG (left) and SIMBA (right). The blue and red contours show the distributions of SFGs and QGs, respectively. The contour levels are the 50th and 90th percentiles. The galaxy distributions are color coded by ﹩{\rm{\Delta }}\mathrm{log}\,{M}_{\mathrm{HI}}﹩ (top panels) and ﹩{\rm{\Delta }}\mathrm{log}\,{M}_{{{\rm{H}}}_{2}}﹩ (bottom panels), respectively. The demarcation lines for different AGN feedback modes are displayed as dotted lines. The orange solid line in the right panel shows the position of f edd = 0.02 where the maximal ejection velocity is achieved.
Paper: Effects of Active Galactic Nucleus Feedback on Cold Gas Depletion and Quenching of Central Galaxies
Authors: Wenlin Ma, Kexin Liu, Hong Guo, Weiguang Cui, Michael G. Jones, Jing Wang, Le Zhang, Romeel Davé
Figure caption: Average H I mass of central galaxies as a function of stellar mass. From left to right, the results are shown for SFGs (blue) and QGs (red) in Illustris, TNG100, and SIMBA, respectively. We show the simulation predictions as solid (SFGs) and dotted (QGs) lines with the shaded regions representing the 1σ error distributions using a bootstrapping method, while the observational measurements of Guo et al. (2021) are displayed with circles for SFGs and squares for QGs.
Paper: Effects of Active Galactic Nucleus Feedback on Cold Gas Depletion and Quenching of Central Galaxies
Authors: Wenlin Ma, Kexin Liu, Hong Guo, Weiguang Cui, Michael G. Jones, Jing Wang, Le Zhang, Romeel Davé
Figure caption: Distribution of Σ1 as functions of M * (left panel) and M BH (right panel) in TNG50. QGs are showed as red crosses in each panel. The black symbols with errors in the left indicate the Σ1,MS for SFGs in TNG50. The blue dashed line represents the best-fitting observational measurements of Σ1,MS in G21 (﹩\mathrm{log}{{\rm{\Sigma }}}_{1,\mathrm{MS}}=0.81\mathrm{log}{M}_{* }+0.607﹩). The probability distribution of all galaxies is represented by the logarithmic color scales.
Paper:  Mangrove: Learning Galaxy Properties from Merger Trees
Authors: Christian Kragh Jespersen, Miles Cranmer, Peter Melchior, Shirley Ho, Rachel S. Somerville, Austen Gabrielpillai
Figure caption: A diagram of Mangrove's architecture for predicting values and the full-covariance matrix. N  t  is the number of targets one wishes to regress. The number of times a given block is repeated is written in the upper right corner of the block. A linear layer is the same as a one-layer MLP. The flow is from left to right, but inside each box, the flow is from top to bottom. Each layer operates with 128 hidden states. The off-diagonal covariances are not included in the work in this paper.
Paper:  Mangrove: Learning Galaxy Properties from Merger Trees
Authors: Christian Kragh Jespersen, Miles Cranmer, Peter Melchior, Shirley Ho, Rachel S. Somerville, Austen Gabrielpillai
Figure caption: A series of sample training curves for regressing M * only. Hard lines are smoothed with an exponential kernel with a strength of 0.6, and the shading represents the actual validation scatters. As can be seen, the training can be noisy, and have short, strong spikes, but eventually converges given enough epochs.
Paper: Deep Learning Based Solar Flare Forecasting Model. II. Influence of Image Resolution
Authors: Sixuan Liu, Long Xu, Zhongrui Zhao, R. Erdélyi, Marianna B. Korsós, Xin Huang
Figure caption: Visualization of network’s interest area. The first row is the input image of different resolutions. The second, third, and fourth rows are the visualization results of different networks. The highlighted areas of the magnetogram, which were relevant for the classification. This active region sample is NOAA 12497 on 2016 February 12.
Paper: Deep Learning Based Solar Flare Forecasting Model. II. Influence of Image Resolution
Authors: Sixuan Liu, Long Xu, Zhongrui Zhao, R. Erdélyi, Marianna B. Korsós, Xin Huang
Figure caption: Flow chart of forecasting network. AlexNet, ResNet-18, and SqueezeNet: three convolutional neural networks (CNNs) are selected to forecast the flaring. The input of each CNN is a magnetogram of different resolution. The output of each CNN is the forecast result on whether a flare will burst in the next 48 hr. We evaluate the performance by synthesizing the forecast results of different networks at different resolutions.
Paper: Deep Learning Based Solar Flare Forecasting Model. II. Influence of Image Resolution
Authors: Sixuan Liu, Long Xu, Zhongrui Zhao, R. Erdélyi, Marianna B. Korsós, Xin Huang
Figure caption: Fire module. This module consists of “Squeeze” and “Expand” two convolutional layers that both connect a ReLU activation function. In this example, the number of filters is set as S = 3, E 1 = 4, and E 2 = 4.
Paper: Deep Learning Based Solar Flare Forecasting Model. II. Influence of Image Resolution
Authors: Sixuan Liu, Long Xu, Zhongrui Zhao, R. Erdélyi, Marianna B. Korsós, Xin Huang
Figure caption: ResNet-18 structure. ResNet-18 contains 18 layers—a convolution layer, max-pooling layer, average pooling layer, and FC layer. Among the convolution layers, there are solid arrows and dashed arrows representing the two residual operations corresponding to Equation (4) and Equation (5).
Paper: Deep Learning Based Solar Flare Forecasting Model. II. Influence of Image Resolution
Authors: Sixuan Liu, Long Xu, Zhongrui Zhao, R. Erdélyi, Marianna B. Korsós, Xin Huang
Figure caption: SqueezeNet structure. SqueezeNet contains 10 layers. The basic modules are almost the same as AlexNet and ResNet-18. Introduced is a new module called the “Fire module,” which is explained in Figure 5.
Paper: Deep Learning Based Solar Flare Forecasting Model. II. Influence of Image Resolution
Authors: Sixuan Liu, Long Xu, Zhongrui Zhao, R. Erdélyi, Marianna B. Korsós, Xin Huang
Figure caption: AUC and TSS curve with image resolution. The x-axis indicates the ratio of resolution reduction. Blue curve represents AlexNet, red curve represents ResNet-18, and green curve represents SqueezeNet.
Paper: Deep Learning Based Solar Flare Forecasting Model. II. Influence of Image Resolution
Authors: Sixuan Liu, Long Xu, Zhongrui Zhao, R. Erdélyi, Marianna B. Korsós, Xin Huang
Figure caption: Loss curve with different epochs. Eight color curves represent different resolutions.
Paper: Standard GRB Spectral Models “Misused”?
Authors: Liang Li
Figure caption: Distributions of α (upper panels), E p (middle panels), and β (lower panel). All are based on statistical significance S ≥ 20 (944 spectra). The spectra separated by the better models via to check their posterior plots. For each spectral parameter, the left panel shows the model preferred cases while the right panel displays the model misused cases. The BAND-preferred spectra is indicated by gray color while those of the COMP-preferred spectra reindicated by orange color.
Paper: Standard GRB Spectral Models “Misused”?
Authors: Liang Li
Figure caption: Same as Figure 6 but for the spectra grouped based on DIC statistic. The green line indicated the line of death for the synchrotron emission (α = −2/3).
Paper: Standard GRB Spectral Models “Misused”?
Authors: Liang Li
Figure caption: Comparison of the fitting of the same spectral data using BAND and COMP models. Left panel: COMP is the statistically preferred model for the time interval between 86.338 and 86.877 s of GRB 120728. Right panel: BAND is the statistically preferred model for the time interval between 69.274 and 71.015 s of GRB 120728.
Paper: Standard GRB Spectral Models “Misused”?
Authors: Liang Li
Figure caption: Bayesian MCMC spectral fits to the data in one time bin (between 24.215 and 25.597 s) of GRB 171227 comparing the BAND with COMP models. The left panel shows the BAND fit to the data with a well-constrained β while the right panel displays the COMP fit to the same data. The plots shows a COMP-preferred spectrum, with ΔDIC = 1.7.
Paper: Standard GRB Spectral Models “Misused”?
Authors: Liang Li
Figure caption: Comparison of the same spectral parameters between BAND and COMP: the α BAND–α COMP (left panel), ﹩{E}_{{\rm{p}}}^{\mathrm{BAND}}﹩–﹩{E}_{{\rm{p}}}^{\mathrm{COMP}}﹩ (middle panel), and F BAND–F COMP (right panel) plottings. Upper panels: for the β-based categories. Lower panels: for the DIC-based groups.
Paper: Standard GRB Spectral Models “Misused”?
Authors: Liang Li
Figure caption: Distributions of fitted values of α obtained from the BAND and COMP as compared to their effective values α eff, computed at 8 keV using the Equation (2) in Preece et al. (1998). Left panel: for the global spectra. Right panel: for the β-based spectra.
Paper: Standard GRB Spectral Models “Misused”?
Authors: Liang Li
Figure caption: Same as Figure 1 but for another time bin (between 17.648 and 17.820 s) of GRB 171227. The left panel shows the BAND fit to the data with a well-constrained β while the right panel displays the COMP fit to the same data. The plots shows a BAND-preferred spectrum, with ΔDIC = −24.8.
Paper: Standard GRB Spectral Models “Misused”?
Authors: Liang Li
Figure caption: Distribution of ΔDIC. The different groups are overlaid by different colors: Group I (blue), Group II (magenta), Group III (sky blue), and Group IV (gray). While the global distribution is shown by the green curve.
Paper: Standard GRB Spectral Models “Misused”?
Authors: Liang Li
Figure caption: Same as Figure 4 but for the simulated spectra. The left panel is the fittings for the simulated COMP-like spectrum while the right panel is the fittings for the simulated BAND-like spectrum.
Paper: Influence of Neutrino–Nuclear Reactions on the Abundance of 74Se
Authors: N. Song, S. Zhang, Z. H. Li, G. X. Li, Z. C. Gao, H. K. Wang
Figure caption: Neutrino energy-dependent cross sections of the 74Ge (ν  e , e −) 74As reaction. The black and red solid lines represent the discrete cross sections calculated using experimental and theoretical B(GT), respectively.
Paper: Influence of Neutrino–Nuclear Reactions on the Abundance of 74Se
Authors: N. Song, S. Zhang, Z. H. Li, G. X. Li, Z. C. Gao, H. K. Wang
Figure caption: The abundance ratios of 74Se and 74Ge in the process 74Ge (ν  e , e −) 74As vary with the initial radius r 0 for different average shock velocities at neutrino temperature T  ν  = 2.8 MeV.
Paper: Influence of Neutrino–Nuclear Reactions on the Abundance of 74Se
Authors: N. Song, S. Zhang, Z. H. Li, G. X. Li, Z. C. Gao, H. K. Wang
Figure caption: The neutrino energy-weighted cross sections for the 74Ge (ν  e , e −) 74As reaction vary with neutrino temperature. The red solid line represents the discrete cross sections. The green solid line shows the cross section of the transitions to the high-lying resonance states. The blue solid line represents the contribution of the multipole transitions to the total cross section. The black solid line represents the total cross sections of 74Ge (ν  e , e −) 74As.
Paper: Influence of Neutrino–Nuclear Reactions on the Abundance of 74Se
Authors: N. Song, S. Zhang, Z. H. Li, G. X. Li, Z. C. Gao, H. K. Wang
Figure caption: The abundance ratios of 74Se and 74Ge in the process 74Ge (ν  e , e −) 74As varies with the initial radius r 0 for different neutrino temperatures, with average shock velocity υ sh = 5000 km s−1.
Paper: Influence of Neutrino–Nuclear Reactions on the Abundance of 74Se
Authors: N. Song, S. Zhang, Z. H. Li, G. X. Li, Z. C. Gao, H. K. Wang
Figure caption: The reaction rate of 74As (γ, p) 73Ge reaction varies with temperature.
Paper: Discovery of a Double Sequence of Blue Straggler Stars in the Core-collapsed Globular Cluster NGC 6256
Authors: Mario Cadelano, Francesco R. Ferraro, Emanuele Dalessandro, Barbara Lanzoni, Cristina Pallanca, Sara Saracino
Figure caption: Left: observed (empty circles) and background-subtracted (filled circles) density profile of NGC 6256. The dashed red curve is the best-fit King model to the cluster density profile, and the red stripe marks the envelope of the ±1σ solutions. The dashed and dotted–dashed vertical lines mark the best-fit cluster’s core and half-mass radii, respectively, and their corresponding 1σ uncertainties are represented with the gray stripes. The best-fit values of some structural parameters (see text) are also labeled. The bottom panel shows the residuals between the best-fit King model and the cluster density profile. Right: same as in the left panel, but with the three innermost points colored in red to highlight the presence of a stellar density cusp. These points have been fitted with a power-law function, shown as a red solid line, having a slope α CUSP labeled in the figure legend. The best-fit King model to the profile obtained by excluding the three inner points (black solid circles) is shown with the red dashed curve and red stripe, and its corresponding parameters are labeled in the legend.
Paper: Discovery of a Double Sequence of Blue Straggler Stars in the Core-collapsed Globular Cluster NGC 6256
Authors: Mario Cadelano, Francesco R. Ferraro, Emanuele Dalessandro, Barbara Lanzoni, Cristina Pallanca, Sara Saracino
Figure caption: FOVs covered by the data sets used in this work, plotted with respect to the cluster’s center (black cross) quoted in Cadelano et al. (2020b). The gray dots are the observed stars. The violet and cyan thick lines mark the FOVs of the WFC3 and ACS data sets, respectively. The black solid line marks the edge of a portion of the wide-field VIRCAM data set. The inner and outer dashed circles are the cluster’s half-mass and tidal radii, respectively, as determined in Section 3.
Paper: Discovery of a Double Sequence of Blue Straggler Stars in the Core-collapsed Globular Cluster NGC 6256
Authors: Mario Cadelano, Francesco R. Ferraro, Emanuele Dalessandro, Barbara Lanzoni, Cristina Pallanca, Sara Saracino
Figure caption: Comparison between the background-subtracted density profile of NGC 6256 determined in this work (black circles), and that obtained by Cohen et al. (2021), vertically rescaled to match the former (gray circles). The solid and dashed lines are, respectively, the best-fit King models determined here and in Cohen et al. (2021).
Paper: Observational Analysis and Numerical Modeling of the Solar Wind Fluctuation Spectra during Intervals of Plasma Instability
Authors: S. A. Markovskii, Bernard J. Vasquez
Figure caption: Distribution functions f of the relative rms amplitude A for (a) the intervals with power elevations (magenta line) and (b) the solar wind in general (black line). The distributions are normalized to the total number of intervals in each case. The green line (c) is the distribution with power elevations normalized to the distribution in the solar wind in general.
Paper: Observational Analysis and Numerical Modeling of the Solar Wind Fluctuation Spectra during Intervals of Plasma Instability
Authors: S. A. Markovskii, Bernard J. Vasquez
Figure caption: Reduced magnetic power spectra from (a) the observational data, (b) the simulation with A = 0 (blue line), (c) A = 0.038 (magenta line), and (d) A = 0.072 (green line).
Paper: Discovery of a Double Sequence of Blue Straggler Stars in the Core-collapsed Globular Cluster NGC 6256
Authors: Mario Cadelano, Francesco R. Ferraro, Emanuele Dalessandro, Barbara Lanzoni, Cristina Pallanca, Sara Saracino
Figure caption: Left panel: (m F555W, m F555W − m F814W) CMD of NGC 6256 as obtained from the first epoch, WFC3 observations (from Cadelano et al. 2020b). Middle panel: (m F606W, m F606W − m F814W) CMD obtained from the second epoch, ACS observations. Both first- and second-epoch magnitudes are corrected for differential reddening. Right panel: (J, J − Ks) CMD of the cluster obtained from the VIRCAM observations.
Paper: Observational Analysis and Numerical Modeling of the Solar Wind Fluctuation Spectra during Intervals of Plasma Instability
Authors: S. A. Markovskii, Bernard J. Vasquez
Figure caption: Distribution functions f of the angle between the mean magnetic field and the spacecraft sampling direction for (a) the intervals with power elevations (magenta line) and (b) the solar wind in general (black line). The distributions are normalized to the total number of intervals in each case.
Paper: Observational Analysis and Numerical Modeling of the Solar Wind Fluctuation Spectra during Intervals of Plasma Instability
Authors: S. A. Markovskii, Bernard J. Vasquez
Figure caption: Time dependence of the total intensity I of the unstable magnetic fluctuations, normalized to ﹩{B}_{0}^{2},﹩ corresponding to an initial turbulence amplitude of (a) A = 0 (black line), (b) A = 0.038 (green line), and (c) A = 0.072 (magenta line).
Paper: One-dimensional Numerical Study on Ignition of the Helium Envelope in Dynamical Accretion during the Double-degenerate Merger
Authors: Kazuya Iwata, Keiichi Maeda
Figure caption: Ignition conditions as compared to the analytical thresholds for deflagration and detonation, for a pure helium envelope proposed by Woosley & Kasen (2011; their Equations (8) and (7), respectively). The dashed line is the actual limit derived from their simulations between detonation and the other burning regimes. The symbols are for our models. Filled symbols denote the results for the pure helium envelope, and open ones are for the mixed envelope. Different symbols are used for the cases of detonation (star), shocked subsonic flame with a limiter of ΔlnT max = 0.20 (circles), shocked subsonic flame w/o limiter (squares), isobaric ignition (triangles), and no ignition (diamonds).
Paper: One-dimensional Numerical Study on Ignition of the Helium Envelope in Dynamical Accretion during the Double-degenerate Merger
Authors: Kazuya Iwata, Keiichi Maeda
Figure caption: Temporal evolution of the systems in the three burning regimes. Two separate figures are provided for each regime to show the profiles of temperature, density, velocity, and helium mass fraction for five different instants, which are distinguished by the colors of the lines. For the detonation regime, two examples are shown with different treatments of the nuclear reactions (i.e., the burning limiter as explained in Section 3.4; upper two panels). The envelope base is located at the left end, and the accretion stream is partly shown close to the right end.